Will AI replace Gambling Change Persons and Booth Cashiers?

Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Gambling Change Persons and Booth Cashiers (United States, SOC 41-2012), 18% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 21 out of 100 (range 19–25, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Gambling Change Persons and Booth Cashiers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Reconcile daily summaries of transactions to balance books” (88/100, very high); “Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations” (75/100, high); “Calculate the value of chips won or lost by players” (50/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Gambling Change Persons and Booth Cashiers” stay human?

About 71% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Clean casino areas” (0/100, minimal); “Exchange money, credit, tickets, or casino chips and make change for customers” (0/100, minimal); “Furnish change persons with a money bank at the start of each shift” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Gambling Change Persons and Booth Cashiers” do about AI?

Start from the ledger rather than the headline: 18% of this job's weighted core work is exposed, and roughly 71% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Gambling Change Persons and Booth Cashiers calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 13 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Gambling Change Persons and Booth Cashiers

keeping accurate records of monetary exchanges, counting money and audit money drawers and checking identifications to verify age of players. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: keeping accurate records of monetary exchanges. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 18%
changing shape: 11%
staying human: 71%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 21 out of 100 (19–25 allowing for uncertainty): low exposure, across 13 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for gambling change persons and booth cashiers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

That slice is not coming back; the core of the job, exchanging money, credit, tickets or casino chips and making change for customers, stays yours. The tools change hands, the accountability doesn't.

Your week, as this page understands it

Exchange coins, tokens, and chips for patrons' money. May issue payoffs and obtain customer's signature on receipt. May operate a booth in the slot machine area and furnish change persons with money bank at the start of the shift, or count and audit money in drawers. The job title says “gambling change persons” or “booth cashiers”: officially one job, two names. The real job is the part underneath: exchanging money, credit, tickets or casino chips and making change for customers. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But gambling change persons and booth cashiers is not one task. It is 13 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is exchanging money, credit, tickets or casino chips and making change for customers, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 18%
changing shape: 11%
staying human: 71%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

2 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Keeping accurate records of monetary exchanges
This is reading one thing and writing another: accurate records of monetary exchanges in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Logging exchanges and reconciling transactions is record-keeping software already does accurately.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Reconciling daily summaries of transactions to balance books
This is reading one thing and writing another: summaries of transactions in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Reconcile daily summaries of transactions to balance books.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Balancing daily transaction summaries against the books is exactly what accounting software does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Calculating the value of chips won or lost by players
The software now makes the first pass at the value of chips won, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · Supplemental
Source: “Calculate the value of chips won or lost by players.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The arithmetic is simple, but someone still has to count the physical chips.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Accepting credit applications and verifying credit references to provide check-cashing authorization or to establish house credit accounts
The software now makes the first pass at credit applications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Accept credit applications and verify credit references to provide check-cashing authorization or to establish house credit accounts.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking credit references against an application is document work software handles, with staff approving.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

9 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Exchanging money, credit, tickets or casino chips and making change for customers
This work happens in the physical world: money, credit, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Exchange money, credit, tickets, or casino chips and make change for customers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Handing over cash, chips and tickets means physically dealing with a customer at the window.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Counting money and audit money drawers
This work happens in the physical world: money, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Count money and audit money drawers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Counting cash and auditing drawers means physically handling the money.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Checking identifications to verify age of players
This work happens in the physical world: identifications, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check identifications to verify age of players.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (5–19 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Checking a player's ID means looking at the document and the person in front of you.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Maintaining cage security according to rules
This work happens in the physical world: cage security, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Maintain cage security according to rules.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Keeping the cage secure depends on someone being physically present watching it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Obtaining customers' signatures on receipts when winnings exceed the amount held in a slot machine
This work happens in the physical world: customers' signatures, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Obtain customers' signatures on receipts when winnings exceed the amount held in a slot machine.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Getting a signature on a receipt requires the customer and the paper in the same room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Listening for jackpot alarm bells and issuing payoffs to winners
This work happens in the physical world: jackpot alarm bells, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Listen for jackpot alarm bells and issue payoffs to winners.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Responding to a jackpot alarm and paying a winner needs someone on the floor.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

Show the other 3 tasks

Selling gambling chips, tokens or tickets to patrons or to other workers for resale to patrons
staying human
This work happens in the physical world: chips, tokens or tickets, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Selling chips and tokens over the counter is a hands-on cash transaction.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Furnishing change persons with a money bank at the start of each shift
staying human
This work happens in the physical world: change persons, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Furnish change persons with a money bank at the start of each shift.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Handing over a cash bank at shift start is physical money movement.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cleaning casino areas
staying human
This work happens in the physical world: casino areas, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean casino areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning the casino floor is physical work in the room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $36,220a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 21,530in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: accurate records of monetary exchanges in, a record out. The rows above are exactly that shape: keeping accurate records of monetary exchanges and reconciling daily summaries of transactions to balance books. What it cannot do is be there in the room, and that is still where money, credit get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: exchanging money, credit, tickets or casino chips and making change for customers is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 18% of this job's task weight sits in rows the software is already learning, 11% in rows that change shape rather than disappear, and 71% in rows it is nowhere near. That is the position, measured across 13 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Keeping accurate records of monetary exchanges is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to accurate records of monetary exchanges, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether money, credit are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near calculating the value of chips won or lost by players. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to gambling change persons and booth cashiers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was gambling cage workers: only about 25% of its durable work is work you already do. And on the numbers you do not need one. This job scores 21/100 here, with only 18% of the task list in the top band, and “exchange money, credit, tickets, or casino chips and make change for customers” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Gambling Cage Workers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “maintain cage security according to rules”. Across the whole of both lists that adds up to about 25% of the work in that job the software is not taking.
Why I am not recommending it: It is closer than most, and still not close enough: about 25% of that job's durable work is already yours, against the 35% I want to see before I will call something a route.
Look at that job’s page anyway →
Gambling Dealers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange money, credit, tickets, or casino chips and make change for customers, and their equivalent is to exchange paper currency for playing chips or coin money. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Driver/Sales Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already exchange money, credit, tickets, or casino chips and make change for customers, and their equivalent is to collect money from customers, make change, and record transactions on customer receipts. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 18% of its task weight, across 13 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like exchanging money, credit, tickets or casino chips and making change for customers arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Retail cashiers and check-out operators is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Retail cashiers and check-out operators. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for gambling change persons / booth cashiers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 18% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for gambling change persons / booth cashiers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for gambling change persons / booth cashiers launches. Nothing else.

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No Space for gambling change persons / booth cashiers yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for gambling change persons / booth cashiers existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for gambling change persons / booth cashiers launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Gambling Change Persons and Booth Cashiers?: Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Gambling Change Persons and Booth Cashiers (United States, SOC 41-2012), 18% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 21 out of 100 (range 19–25, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Gambling Change Persons and Booth Cashiers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Reconcile daily summaries of transactions to balance books” (88/100, very high); “Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations” (75/100, high); “Calculate the value of chips won or lost by players” (50/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Gambling Change Persons and Booth Cashiers” stay human?: About 71% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Clean casino areas” (0/100, minimal); “Exchange money, credit, tickets, or casino chips and make change for customers” (0/100, minimal); “Furnish change persons with a money bank at the start of each shift” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Gambling Change Persons and Booth Cashiers” do about AI?: Start from the ledger rather than the headline: 18% of this job's weighted core work is exposed, and roughly 71% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Gambling Change Persons and Booth Cashiers calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 13 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

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https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.